Mots-Clés
Bioinformatique
biologie computationnelle
cancer
transcriptomique
tumeurs pédiatriques cérébrales
cellule unique
single-cell
multi-omique
Description
Context
The PhD thesis will be performed as part of the Computational Biology and Integrative Genomics of Cancer team at Institut Curie, co-supervised by Dr. Florence Cavalli, group leader, and Dr. Gautier Stoll researcher member.
Institut Curie is a major player in the research and fight against cancer. It consists of a Hospital group and a Research Center of more than 1000 employees with a strong international representativeness. The objective of the Research Center is to develop basic research and to use the knowledge produced to improve the diagnosis, prognosis, and therapeutics of cancers as part of the continuum between basic research and innovation serving the patient.
The Cavalli Lab is part of the Computational Oncology Unit (U1331 INSERM, Mines ParisTech, Institut Curie) at Institut Curie, which consists of ~90 researchers and students. It is a very active and growing interdisciplinary team of bioinformaticians, biologists, physicians, mathematicians, statisticians, physicists, and computer scientists.
The Cavalli Lab , investigates tumor heterogeneity, targeting clinically relevant questions. The goal of our genomic approaches is to explore clinically relevant aspects of brain tumor biology. We pursue this goal using patient samples profiling, investigating temporal and intra-tumoral/spatial heterogeneity as well as tumor/tumor microenvironment interactions in adult gliomas and pediatric brain tumors. Projects in the Cavalli lab are developed within a dynamic and collaborative environment with other researchers and clinicians at Institut Curie and beyond.
Project
This project focuses pediatric embryonal brain tumors with BCOR internal tandem duplication (BCOR ITD). These tumors occur in very young children. To date, there is no standard therapeutic protocol for these tumors and the prognosis remains bad.
Following bulk mRNA profiling, very little is known regarding the oncogenic process driving tumor growth. The intra-tumoral heterogeneity observed at the histological level has not been further studied. It is therefore essential to characterize them at single-cell resolution to gain knowledge on their functional intra-heterogeneity that is likely to be the main cause of treatment resistance.
This PhD project aims to perform an extended analysis of a unique multiomic dataset generated from frozen patient samples composed of single-nucleus RNA-seq/ATAC-seq. The PhD student will perform advanced bioinformatics and integrative analysis on single-cell data and develop its own analysis workflows to answer biological questions.
This computational oncology project will reveal for the first time the tumor cell populations present in the tumors, their master regulators, predict the cell of origin of the tumors and evaluate the tumor cells plasticity.
Therefore, this project aims to bring an unprecedented functional characterization of intra-tumoral heterogeneity as well as the microenvironment of these tumors. This will ultimately enable a jump forward in identifying key mechanisms responsible for tumor progression, providing novel rationales for the future development of mouse models to further study them and for the development of new targeted therapies.
This project is part of a larger collaborative project with wet lab biologists, oncologists and neuropathologists at Institut Curie and Ste-Anne hospital focusing on this rare pediatric brain tumor type. The PhD student will therefore be involved in multidisciplinary interactions. It is a unique opportunity to apply high-level computational biology analysis in a context of high clinical importance, in close collaboration with biologists and medical doctors.
Profile
- M2 master diploma or equivalent in bioinformatics, statistics or computer science with knowledge or interest in biology
- Strong foundation of knowledge in one or more of the following: genomics, cancer biology, statistics
- Computational and programming skills (R, Python…)
- Experience in NGS data analysis.
- Experience in single-cell data analysis
- Professional English language
- Team spirit